This is the Terrier Team blog. It is managed by several members of the Terrier Team at the University of Glasgow. It is used to publicise our research projects, to post news about various research work and activities performed by the team, as well as to share information and thoughts on information retrieval and search engines issues.

The
thesis tackles the problem of ambiguity in web search queries. In particular, with
the enormous size of the Web, a misunderstanding of the information need
underlying an ambiguous query can misguide the search engine, ultimately
leading the user to abandon the originally submitted query. To overcome this
problem, a sensible approach is to diversify the documents retrieved for the
user's query. As a result, the likelihood that at least one of these documents
will satisfy the user's actual information need is increased.

In
the thesis, we argue that an ambiguous query should be seen as representing not
one, but multiple information needs. Based upon this premise, we propose xQuAD – Explicit Query Aspect Diversification,
a novel probabilistic framework for search result diversification. In
particular, the xQuAD framework naturally models several dimensions of the search
result diversification problem in a principled yet practical manner. To this
end, the framework represents the possible information needs underlying a query
as a set of keyword-based sub-queries.
Moreover, xQuAD accounts for the overall coverage
of each retrieved document with respect to the identified sub-queries, so as to
rank highly diverse documents first. In addition, it accounts for how well each
sub-query is covered by the other retrieved documents, so as to promote novelty– and hence penalise
redundancy – in the ranking. The framework also models the importance of each of the identified sub-queries, so as to
appropriately cater for the interests of the user population when diversifying
the retrieved documents. Finally, since not all queries are equally ambiguous,
the xQuAD framework caters for the ambiguity level of different queries, so as
to appropriately trade-off relevance for diversity on a per-query
basis.

The
xQuAD framework is general and can be used to instantiate several
diversification models, including the most prominent models described in the
literature. In particular, within xQuAD, each of the aforementioned dimensions
of the search result diversification problem can be tackled in a variety of
ways. In this thesis, as additional contributions besides the xQuAD framework,
we introduce novel machine learning approaches for addressing each of these
dimensions. These include a learning to rank approach for identifying effective sub-queries as query suggestions mined from a query log, an intent-aware
approach for choosing the ranking models most likely to be effective for
estimating the coverage and novelty of multiple documents with respect to a
sub-query, and a selective approach for automatically predicting how much to diversify the documents retrieved for each individual query. In addition, we
perform the first empirical analysis of the role of novelty as a diversification
strategy for web search.

As
demonstrated throughout the thesis, the principles underlying the xQuAD
framework are general, sound, and effective. In particular, to validate the
contributions of this thesis, we thoroughly assess the effectiveness of xQuAD
under the standard experimentation paradigm provided by the diversity task of
the TREC 2009, 2010, and 2011 Web tracks. The results of this investigation
demonstrate the effectiveness of our proposed framework. Indeed, xQuAD attains
consistent and significant improvements in comparison to the most effective
diversification approaches in the literature, and across a range of
experimental conditions, comprising multiple input rankings, multiple sub-query
generation and coverage estimation mechanisms, as well as queries with multiple
levels of ambiguity.

These investigations led to the publication of 12 peer-reviewed research papers and 5 evaluation forum reports directly related to the thesis. Moreover, the thesis opened up directions for other researchers, who deployed and extended the xQuAD framework for different applications, and inspired a series of workshops on Diversity in Document Retrieval as well as a research track at the internationally renown NTCIR forum. From a practical perspective, xQuAD has been subjected to scrutiny from the research community as a regular contender in both TREC and NTCIR. As the winning entry in all editions of the diversity task of the TREC Web track (best cat. B submission in TREC 2009 and TREC 2010; best overall submission in TREC 2011 and TREC 2012), we believe that the xQuAD framework has secured its place in the state-of-the-art.